Title |
CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses
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Published in |
BMC Bioinformatics, February 2018
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DOI | 10.1186/s12859-018-2053-1 |
Pubmed ID | |
Authors |
Pedro S. T. Russo, Gustavo R. Ferreira, Lucas E. Cardozo, Matheus C. Bürger, Raul Arias-Carrasco, Sandra R. Maruyama, Thiago D. C. Hirata, Diógenes S. Lima, Fernando M. Passos, Kiyoshi F. Fukutani, Melissa Lever, João S. Silva, Vinicius Maracaja-Coutinho, Helder I. Nakaya |
Abstract |
The analysis of modular gene co-expression networks is a well-established method commonly used for discovering the systems-level functionality of genes. In addition, these studies provide a basis for the discovery of clinically relevant molecular pathways underlying different diseases and conditions. In this paper, we present a fast and easy-to-use Bioconductor package named CEMiTool that unifies the discovery and the analysis of co-expression modules. Using the same real datasets, we demonstrate that CEMiTool outperforms existing tools, and provides unique results in a user-friendly html report with high quality graphs. Among its features, our tool evaluates whether modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group, as well as it integrates transcriptomic data with interactome information, identifying the potential hubs on each network. We successfully applied CEMiTool to over 1000 transcriptome datasets, and to a new RNA-seq dataset of patients infected with Leishmania, revealing novel insights of the disease's physiopathology. The CEMiTool R package provides users with an easy-to-use method to automatically implement gene co-expression network analyses, obtain key information about the discovered gene modules using additional downstream analyses and retrieve publication-ready results via a high-quality interactive report. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 20% |
Brazil | 3 | 15% |
Chile | 2 | 10% |
Mexico | 2 | 10% |
Georgia | 1 | 5% |
Switzerland | 1 | 5% |
Japan | 1 | 5% |
United Kingdom | 1 | 5% |
Unknown | 5 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 11 | 55% |
Members of the public | 9 | 45% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 390 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 77 | 20% |
Researcher | 73 | 19% |
Student > Bachelor | 54 | 14% |
Student > Master | 48 | 12% |
Student > Postgraduate | 14 | 4% |
Other | 38 | 10% |
Unknown | 86 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 106 | 27% |
Agricultural and Biological Sciences | 81 | 21% |
Medicine and Dentistry | 23 | 6% |
Immunology and Microbiology | 20 | 5% |
Computer Science | 18 | 5% |
Other | 43 | 11% |
Unknown | 99 | 25% |